Hyperspectral Image(HSI)classification based on deep learning has been an attractive area in recent years.However,as a kind of data-driven algorithm,the deep learning method usually requires numerous computational res...Hyperspectral Image(HSI)classification based on deep learning has been an attractive area in recent years.However,as a kind of data-driven algorithm,the deep learning method usually requires numerous computational resources and high-quality labelled datasets,while the expenditures of high-performance computing and data annotation are expensive.In this paper,to reduce the dependence on massive calculation and labelled samples,we propose a deep Double-Channel dense network(DDCD)for Hyperspectral Image Classification.Specifically,we design a 3D Double-Channel dense layer to capture the local and global features of the input.And we propose a Linear Attention Mechanism that is approximate to dot-product attention with much less memory and computational costs.The number of parameters and the consumptions of calculation are observably less than contrapositive deep learning methods,which means DDCD owns simpler architecture and higher efficiency.A series of quantitative experiences on 6 widely used hyperspectral datasets show that the proposed DDCD obtains state-of-the-art performance,even though when the absence of labelled samples is severe.展开更多
A brain-computer interface(BCI)system is one of the most effective ways that translates brain signals into output commands.Different imagery activities can be classified based on the changes inμandβrhythms and their...A brain-computer interface(BCI)system is one of the most effective ways that translates brain signals into output commands.Different imagery activities can be classified based on the changes inμandβrhythms and their spatial distributions.Multi-layer perceptron neural networks(MLP-NNs)are commonly used for classification.Training such MLP-NNs has great importance in a way that has attracted many researchers to this field recently.Conventional methods for training NNs,such as gradient descent and recursive methods,have some disadvantages including low accuracy,slow convergence speed and trapping in local minimums.In this paper,in order to overcome these issues,the MLP-NN trained by a hybrid population-physics-based algorithm,the combination of particle swarm optimization and gravitational search algorithm(PSOGSA),is proposed for our classification problem.To show the advantages of using PSOGSA that trains NNs,this algorithm is compared with other meta-heuristic algorithms such as particle swarm optimization(PSO),gravitational search algorithm(GSA)and new versions of PSO.The metrics that are discussed in this paper are the speed of convergence and classification accuracy metrics.The results show that the proposed algorithm in most subjects of encephalography(EEG)dataset has very better or acceptable performance compared to others.展开更多
This study focuses on the determination of physical and mechanical characteristics based on in vitro tests, by using field samples for the Kampemba urban area in the city of Lubumbashi. At the end of this study, we id...This study focuses on the determination of physical and mechanical characteristics based on in vitro tests, by using field samples for the Kampemba urban area in the city of Lubumbashi. At the end of this study, we identified the soils according to their parameters, and established the geotechnical classification by determining their bearing capacity by the group index method using from the identification tests carried out. By using the AASHTO classification method (American Association for State Highway Transportation Official), the results obtained after our studies revealed five classes of soil: A-2, A-4, A-5, A-6, A-7 in a general way, and particularly eight subgroups of soil: A-2-4, A-2-6, A-2-7, A-4, A-5, A-6, A-7-5 and A-7-6 for the concerned area. The latter has given statistical analysis and deep learning based on multi-layer perceptron, the global values of the physical parameters. It’s about: 31.77% ± 1.05% for the limit of liquidity;18.71% ± 0.76% for the plastic limit;13.06% ± 0.79% for the plasticity index;83.00% ± 3.33% for passing of 2 mm sieve;76.22% ± 3.2% for passing of 400 μm sieve;89.07% ± 2.99% for passing of 4.75 mm sieve;70.62% ± 2.39% passing of 80 μm sieve;1.66 ± 0.61 for the consistency index;<span style="white-space:nowrap;">−</span>0.67 ± 0.62 for the liquidity index and 8 ± 1 for the group index.展开更多
基金National Natural Science Foundations of China(41671452)China Postdoctoral Science Foundation Funded Project(2017M612510)。
文摘Hyperspectral Image(HSI)classification based on deep learning has been an attractive area in recent years.However,as a kind of data-driven algorithm,the deep learning method usually requires numerous computational resources and high-quality labelled datasets,while the expenditures of high-performance computing and data annotation are expensive.In this paper,to reduce the dependence on massive calculation and labelled samples,we propose a deep Double-Channel dense network(DDCD)for Hyperspectral Image Classification.Specifically,we design a 3D Double-Channel dense layer to capture the local and global features of the input.And we propose a Linear Attention Mechanism that is approximate to dot-product attention with much less memory and computational costs.The number of parameters and the consumptions of calculation are observably less than contrapositive deep learning methods,which means DDCD owns simpler architecture and higher efficiency.A series of quantitative experiences on 6 widely used hyperspectral datasets show that the proposed DDCD obtains state-of-the-art performance,even though when the absence of labelled samples is severe.
文摘A brain-computer interface(BCI)system is one of the most effective ways that translates brain signals into output commands.Different imagery activities can be classified based on the changes inμandβrhythms and their spatial distributions.Multi-layer perceptron neural networks(MLP-NNs)are commonly used for classification.Training such MLP-NNs has great importance in a way that has attracted many researchers to this field recently.Conventional methods for training NNs,such as gradient descent and recursive methods,have some disadvantages including low accuracy,slow convergence speed and trapping in local minimums.In this paper,in order to overcome these issues,the MLP-NN trained by a hybrid population-physics-based algorithm,the combination of particle swarm optimization and gravitational search algorithm(PSOGSA),is proposed for our classification problem.To show the advantages of using PSOGSA that trains NNs,this algorithm is compared with other meta-heuristic algorithms such as particle swarm optimization(PSO),gravitational search algorithm(GSA)and new versions of PSO.The metrics that are discussed in this paper are the speed of convergence and classification accuracy metrics.The results show that the proposed algorithm in most subjects of encephalography(EEG)dataset has very better or acceptable performance compared to others.
文摘This study focuses on the determination of physical and mechanical characteristics based on in vitro tests, by using field samples for the Kampemba urban area in the city of Lubumbashi. At the end of this study, we identified the soils according to their parameters, and established the geotechnical classification by determining their bearing capacity by the group index method using from the identification tests carried out. By using the AASHTO classification method (American Association for State Highway Transportation Official), the results obtained after our studies revealed five classes of soil: A-2, A-4, A-5, A-6, A-7 in a general way, and particularly eight subgroups of soil: A-2-4, A-2-6, A-2-7, A-4, A-5, A-6, A-7-5 and A-7-6 for the concerned area. The latter has given statistical analysis and deep learning based on multi-layer perceptron, the global values of the physical parameters. It’s about: 31.77% ± 1.05% for the limit of liquidity;18.71% ± 0.76% for the plastic limit;13.06% ± 0.79% for the plasticity index;83.00% ± 3.33% for passing of 2 mm sieve;76.22% ± 3.2% for passing of 400 μm sieve;89.07% ± 2.99% for passing of 4.75 mm sieve;70.62% ± 2.39% passing of 80 μm sieve;1.66 ± 0.61 for the consistency index;<span style="white-space:nowrap;">−</span>0.67 ± 0.62 for the liquidity index and 8 ± 1 for the group index.
